






import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel



class 				_A			:
    """simple docstring"""

    def __init__(				self      :     List[Any] ,     __SCREAMING_SNAKE_CASE      :     Tuple ,     __SCREAMING_SNAKE_CASE      :     List[str]=99 ,     __SCREAMING_SNAKE_CASE      :     int=13 ,     __SCREAMING_SNAKE_CASE      :     Any=7 ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any]=9 ,     __SCREAMING_SNAKE_CASE      :     Any=True ,     __SCREAMING_SNAKE_CASE      :     Optional[Any]=True ,     __SCREAMING_SNAKE_CASE      :     Optional[Any]=False ,     __SCREAMING_SNAKE_CASE      :     Any=32 ,     __SCREAMING_SNAKE_CASE      :     int=5 ,     __SCREAMING_SNAKE_CASE      :     Optional[int]=4 ,     __SCREAMING_SNAKE_CASE      :     str=37 ,     __SCREAMING_SNAKE_CASE      :     Dict=8 ,     __SCREAMING_SNAKE_CASE      :     Optional[int]=0.1 ,     __SCREAMING_SNAKE_CASE      :     List[str]=0.002 ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any]=1 ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any]=0 ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any]=0 ,     __SCREAMING_SNAKE_CASE      :     Dict=None ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any]=None ,     )      ->							List[Any]:
           __UpperCAmelCase     				=parent
           __UpperCAmelCase     				=batch_size
           __UpperCAmelCase     				=encoder_seq_length
           __UpperCAmelCase     				=decoder_seq_length
           # For common tests
           __UpperCAmelCase     				=self.decoder_seq_length
           __UpperCAmelCase     				=is_training
           __UpperCAmelCase     				=use_attention_mask
           __UpperCAmelCase     				=use_labels
           __UpperCAmelCase     				=vocab_size
           __UpperCAmelCase     				=hidden_size
           __UpperCAmelCase     				=num_hidden_layers
           __UpperCAmelCase     				=num_attention_heads
           __UpperCAmelCase     				=d_ff
           __UpperCAmelCase     				=relative_attention_num_buckets
           __UpperCAmelCase     				=dropout_rate
           __UpperCAmelCase     				=initializer_factor
           __UpperCAmelCase     				=eos_token_id
           __UpperCAmelCase     				=pad_token_id
           __UpperCAmelCase     				=decoder_start_token_id
           __UpperCAmelCase     				=None
           __UpperCAmelCase     				=decoder_layers



    def _a							(				self      :     Dict      )      ->							List[str]:
           return TaConfig.from_pretrained("""google/umt5-base"""      )



    def _a							(				self      :     Union[str, Any] ,     __SCREAMING_SNAKE_CASE      :     str ,     __SCREAMING_SNAKE_CASE      :     Optional[Any] ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any] ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any]=None ,     __SCREAMING_SNAKE_CASE      :     str=None ,     __SCREAMING_SNAKE_CASE      :     List[str]=None ,     __SCREAMING_SNAKE_CASE      :     List[str]=None ,     __SCREAMING_SNAKE_CASE      :     Optional[Any]=None ,     )      ->							Any:
           if attention_mask is None:
                  __UpperCAmelCase     				=input_ids.ne(config.pad_token_id      )
           if decoder_attention_mask is None:
                  __UpperCAmelCase     				=decoder_input_ids.ne(config.pad_token_id      )
           if head_mask is None:
                  __UpperCAmelCase     				=torch.ones(config.num_hidden_layers ,     config.num_attention_heads ,     device=__SCREAMING_SNAKE_CASE      )
           if decoder_head_mask is None:
                  __UpperCAmelCase     				=torch.ones(config.num_decoder_layers ,     config.num_attention_heads ,     device=__SCREAMING_SNAKE_CASE      )
           if cross_attn_head_mask is None:
                  __UpperCAmelCase     				=torch.ones(
                      config.num_decoder_layers ,     config.num_attention_heads ,     device=__SCREAMING_SNAKE_CASE      )
           return {
               "input_ids": input_ids,
               "decoder_input_ids": decoder_input_ids,
               "attention_mask": attention_mask,
               "decoder_attention_mask": decoder_attention_mask,
               "head_mask": head_mask,
               "decoder_head_mask": decoder_head_mask,
               "cross_attn_head_mask": cross_attn_head_mask,
           }



    def _a							(				self      :     Optional[int]      )      ->							Union[str, Any]:
           __UpperCAmelCase     				=ids_tensor([self.batch_size, self.encoder_seq_length] ,     self.vocab_size      )
           __UpperCAmelCase     				=ids_tensor([self.batch_size, self.decoder_seq_length] ,     self.vocab_size      )

           # we need to clamp the input ids here to avoid having pad token in between
           # this is because for NllbMoe the position_ids are prepared such that
           # all pad tokens have pos id = 2 and rest are between 2..seq_length
           # and the seq_length here is seq_length - num_pad_tokens
           # but when using past, there is no way of knowing if the past input ids had
           # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
           # position_ids being off by num_pad_tokens in past input
           __UpperCAmelCase     				=input_ids.clamp(self.pad_token_id + 1      )
           __UpperCAmelCase     				=decoder_input_ids.clamp(self.pad_token_id + 1      )

           __UpperCAmelCase     				=self.get_config()
           __UpperCAmelCase     				=config.num_attention_heads
           __UpperCAmelCase     				=self.prepare_inputs_dict(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      )
           return config, input_dict



    def _a							(				self      :     Optional[Any]      )      ->							Any:
           __UpperCAmelCase				, __UpperCAmelCase     				=self.prepare_config_and_inputs()
           return config, inputs_dict



    def _a							(				self      :     str      )      ->							Optional[int]:
           return TaConfig(
               vocab_size=166 ,     d_model=self.hidden_size ,     d_ff=self.d_ff ,     d_kv=self.hidden_size // self.num_attention_heads ,     num_layers=self.num_hidden_layers ,     num_decoder_layers=self.decoder_layers ,     num_heads=self.num_attention_heads ,     relative_attention_num_buckets=self.relative_attention_num_buckets ,     dropout_rate=self.dropout_rate ,     initializer_factor=self.initializer_factor ,     eos_token_id=self.eos_token_id ,     bos_token_id=self.pad_token_id ,     pad_token_id=self.pad_token_id ,     decoder_start_token_id=self.decoder_start_token_id ,     )



    def _a							(				self      :     Dict      )      ->							List[Any]:
           return TaConfig(
               vocab_size=self.vocab_size ,     d_model=self.hidden_size ,     d_ff=self.d_ff ,     d_kv=self.hidden_size // self.num_attention_heads ,     num_layers=self.num_hidden_layers ,     num_decoder_layers=self.decoder_layers ,     num_heads=self.num_attention_heads ,     relative_attention_num_buckets=self.relative_attention_num_buckets ,     dropout_rate=self.dropout_rate ,     initializer_factor=self.initializer_factor ,     eos_token_id=self.eos_token_id ,     bos_token_id=self.pad_token_id ,     pad_token_id=self.pad_token_id ,     decoder_start_token_id=self.decoder_start_token_id ,     )



    def _a							(				self      :     List[Any] ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any] ,     __SCREAMING_SNAKE_CASE      :     List[str] ,     __SCREAMING_SNAKE_CASE      :     List[Any] ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any] ,     __SCREAMING_SNAKE_CASE      :     Optional[Any] ,     __SCREAMING_SNAKE_CASE      :     Optional[int] ,     )      ->							List[Any]:
           __UpperCAmelCase     				=UMTaModel(config=__SCREAMING_SNAKE_CASE      )
           model.to(__SCREAMING_SNAKE_CASE      )
           model.eval()
           __UpperCAmelCase     				=model(
               input_ids=__SCREAMING_SNAKE_CASE ,     decoder_input_ids=__SCREAMING_SNAKE_CASE ,     attention_mask=__SCREAMING_SNAKE_CASE ,     decoder_attention_mask=__SCREAMING_SNAKE_CASE ,     )
           __UpperCAmelCase     				=model(input_ids=__SCREAMING_SNAKE_CASE ,     decoder_input_ids=__SCREAMING_SNAKE_CASE      )
           __UpperCAmelCase     				=result.last_hidden_state
           __UpperCAmelCase     				=result.past_key_values
           __UpperCAmelCase     				=result.encoder_last_hidden_state

           self.parent.assertEqual(encoder_output.size() ,     (self.batch_size, self.encoder_seq_length, self.hidden_size)      )
           self.parent.assertEqual(decoder_output.size() ,     (self.batch_size, self.decoder_seq_length, self.hidden_size)      )
           # There should be `num_layers` key value embeddings stored in decoder_past
           self.parent.assertEqual(len(__SCREAMING_SNAKE_CASE      ) ,     config.num_layers      )
           # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
           self.parent.assertEqual(len(decoder_past[0]      ) ,     4      )



    def _a							(				self      :     int ,     __SCREAMING_SNAKE_CASE      :     Tuple ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any] ,     __SCREAMING_SNAKE_CASE      :     Tuple ,     __SCREAMING_SNAKE_CASE      :     str ,     __SCREAMING_SNAKE_CASE      :     Dict ,     __SCREAMING_SNAKE_CASE      :     Tuple ,     )      ->							Dict:
           __UpperCAmelCase     				=UMTaModel(config=__SCREAMING_SNAKE_CASE      ).get_decoder().to(__SCREAMING_SNAKE_CASE      ).eval()
           # first forward pass
           __UpperCAmelCase     				=model(__SCREAMING_SNAKE_CASE ,     use_cache=__SCREAMING_SNAKE_CASE      )
           __UpperCAmelCase     				=model(__SCREAMING_SNAKE_CASE      )
           __UpperCAmelCase     				=model(__SCREAMING_SNAKE_CASE ,     use_cache=__SCREAMING_SNAKE_CASE      )

           self.parent.assertTrue(len(__SCREAMING_SNAKE_CASE      ) == len(__SCREAMING_SNAKE_CASE      )      )
           self.parent.assertTrue(len(__SCREAMING_SNAKE_CASE      ) == len(__SCREAMING_SNAKE_CASE      ) + 1      )

           __UpperCAmelCase				, __UpperCAmelCase     				=outputs.to_tuple()

           # create hypothetical next token and extent to next_input_ids
           __UpperCAmelCase     				=ids_tensor((self.batch_size, 1) ,     config.vocab_size      )

           # append to next input_ids and
           __UpperCAmelCase     				=torch.cat([input_ids, next_tokens] ,     dim=-1      )

           __UpperCAmelCase     				=model(__SCREAMING_SNAKE_CASE      )["""last_hidden_state"""]
           __UpperCAmelCase     				=model(__SCREAMING_SNAKE_CASE ,     past_key_values=__SCREAMING_SNAKE_CASE      )["""last_hidden_state"""]

           # select random slice
           __UpperCAmelCase     				=ids_tensor((1,) ,     output_from_past.shape[-1]      ).item()
           __UpperCAmelCase     				=output_from_no_past[:, -1, random_slice_idx].detach()
           __UpperCAmelCase     				=output_from_past[:, 0, random_slice_idx].detach()

           # test that outputs are equal for slice
           self.parent.assertTrue(torch.allclose(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     atol=1e-3      )      )



    def _a							(				self      :     List[str] ,     __SCREAMING_SNAKE_CASE      :     List[Any] ,     __SCREAMING_SNAKE_CASE      :     Any ,     )      ->							Any:
           __UpperCAmelCase     				=UMTaModel(config=__SCREAMING_SNAKE_CASE      ).to(__SCREAMING_SNAKE_CASE      ).half().eval()
           __UpperCAmelCase     				=model(**__SCREAMING_SNAKE_CASE      )["""last_hidden_state"""]
           self.parent.assertFalse(torch.isnan(__SCREAMING_SNAKE_CASE      ).any().item()      )




@require_torch
class 				_A			(     UpperCamelCase					,					UpperCamelCase					,					UpperCamelCase					,					unittest.TestCase				):
    """simple docstring"""

    lowerCamelCase	:							Optional[Any]				= (
        (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
    )
    lowerCamelCase	:							str				= (UMTaForConditionalGeneration,) if is_torch_available() else ()
    lowerCamelCase	:							Tuple				= (
        {
            'conversational': UMTaForConditionalGeneration,
            'feature-extraction': UMTaModel,
            'summarization': UMTaForConditionalGeneration,
            'text2text-generation': UMTaForConditionalGeneration,
            'translation': UMTaForConditionalGeneration,
            'question-answering': UMTaForQuestionAnswering,
        }
        if is_torch_available()
        else {}
    )
    lowerCamelCase	:							Dict				= True
    lowerCamelCase	:							Optional[Any]				= False
    lowerCamelCase	:							List[str]				= False
    lowerCamelCase	:							int				= True
    lowerCamelCase	:							Optional[Any]				= True
    # The small UMT5 model needs higher percentages for CPU/MP tests
    lowerCamelCase	:							Dict				= [0.8, 0.9]



    def _a							(				self      :     int      )      ->							Any:
           __UpperCAmelCase     				=UMTaModelTester(self      )



    @unittest.skip("""Test has a segmentation fault on torch 1.8.0"""      )
    def _a							(				self      :     str      )      ->							Any:
           __UpperCAmelCase     				=self.model_tester.prepare_config_and_inputs()
           __UpperCAmelCase     				=UMTaModel(config_and_inputs[0]      ).to(__SCREAMING_SNAKE_CASE      )
           with tempfile.TemporaryDirectory() as tmpdirname:
                  torch.onnx.export(
                      __SCREAMING_SNAKE_CASE ,     (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]) ,     f'''{tmpdirname}/t5_test.onnx''' ,     export_params=__SCREAMING_SNAKE_CASE ,     opset_version=9 ,     input_names=["""input_ids""", """decoder_input_ids"""] ,     )



    @unittest.skipIf(torch_device == """cpu""" ,     """Cant do half precision"""      )
    def _a							(				self      :     int      )      ->							Dict:
           __UpperCAmelCase     				=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model_fpaa_forward(*__SCREAMING_SNAKE_CASE      )



    def _a							(				self      :     Dict      )      ->							Union[str, Any]:
           __UpperCAmelCase     				=["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""]
           __UpperCAmelCase     				=self.model_tester.prepare_config_and_inputs()
           __UpperCAmelCase     				=config_and_inputs[0]
           __UpperCAmelCase     				=UMTaForConditionalGeneration(__SCREAMING_SNAKE_CASE      ).eval()
           model.to(__SCREAMING_SNAKE_CASE      )

           __UpperCAmelCase     				={
               """head_mask""": torch.zeros(config.num_layers ,     config.num_heads ,     device=__SCREAMING_SNAKE_CASE      ),
               """decoder_head_mask""": torch.zeros(config.num_decoder_layers ,     config.num_heads ,     device=__SCREAMING_SNAKE_CASE      ),
               """cross_attn_head_mask""": torch.zeros(config.num_decoder_layers ,     config.num_heads ,     device=__SCREAMING_SNAKE_CASE      ),
           }

           for attn_name, (name, mask) in zip(__SCREAMING_SNAKE_CASE ,     head_masking.items()      ):
                  __UpperCAmelCase     				={name: mask}
                  # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
                  if name == "head_mask":
                         __UpperCAmelCase     				=torch.ones(
                             config.num_decoder_layers ,     config.num_heads ,     device=__SCREAMING_SNAKE_CASE      )

                  __UpperCAmelCase     				=model.generate(
                      config_and_inputs[1]["""input_ids"""] ,     num_beams=1 ,     max_length=3 ,     output_attentions=__SCREAMING_SNAKE_CASE ,     return_dict_in_generate=__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE ,     )
                  # We check the state of decoder_attentions and cross_attentions just from the last step
                  __UpperCAmelCase     				=out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
                  self.assertEqual(sum([w.sum().item() for w in attn_weights]      ) ,     0.0      )



    @unittest.skip("""Does not work on the tiny model as we keep hitting edge cases."""      )
    def _a							(				self      :     str      )      ->							str:
           pass




@require_torch
@require_sentencepiece
@require_tokenizers
class 				_A			(     unittest.TestCase				):
    """simple docstring"""

    @slow
    @unittest.skip(
        """Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged"""      )
    def _a							(				self      :     List[Any]      )      ->							Dict:
           __UpperCAmelCase     				=UMTaForConditionalGeneration.from_pretrained("""google/umt5-small""" ,     return_dict=__SCREAMING_SNAKE_CASE      ).to(__SCREAMING_SNAKE_CASE      )
           __UpperCAmelCase     				=AutoTokenizer.from_pretrained("""google/umt5-small""" ,     use_fast=__SCREAMING_SNAKE_CASE ,     legacy=__SCREAMING_SNAKE_CASE      )
           __UpperCAmelCase     				=[
               """Bonjour monsieur <extra_id_0> bien <extra_id_1>.""",
               """No se como puedo <extra_id_0>.""",
               """This is the reason why we <extra_id_0> them.""",
               """The <extra_id_0> walks in <extra_id_1>, seats""",
               """A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.""",
           ]
           __UpperCAmelCase     				=tokenizer(__SCREAMING_SNAKE_CASE ,     return_tensors="""pt""" ,     padding=__SCREAMING_SNAKE_CASE      ).input_ids
           # fmt: off
           __UpperCAmelCase     				=torch.tensor(
               [
                   [ 38530, 210703, 256299, 1410, 256298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                   [   826, 321, 671, 25922, 256299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                   [  1460, 339, 312, 19014, 10620, 758, 256299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
                   [   517, 256299, 14869, 281, 301, 256298, 275, 119983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
                   [   320, 256299, 14869, 281, 2234, 289, 2275, 333,61391, 289, 256298, 543, 256297, 168714, 329, 256296,274, 1],
               ]      )
           # fmt: on
           torch.testing.assert_allclose(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      )

           __UpperCAmelCase     				=model.generate(input_ids.to(__SCREAMING_SNAKE_CASE      )      )
           __UpperCAmelCase     				=[
               """<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>""",
               """<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
               """<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
               """<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
               """<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
           ]
           __UpperCAmelCase     				=tokenizer.batch_decode(__SCREAMING_SNAKE_CASE      )
           self.assertEqual(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      )






import unittest

from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available

from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin


if is_bsa_available():
		from transformers import MarkupLMFeatureExtractor




class 					lowerCamelCase__				(							unittest.TestCase):




	'''simple docstring'''




	def __init__(   self						:Optional[int]	,  a						:List[str]		)						->					Union[str, Any]:
								__UpperCamelCase      :  Dict       =   parent







	def   _lowerCamelCase							(   self						:Dict		)						->					int:
								return {}




def       _SCREAMING_SNAKE_CASE   (						)   ->       Optional[Any]:






							'''simple docstring'''





							__UpperCamelCase      :  Union[str, Any]       =   "<HTML>\n\n    <HEAD>\n    <TITLE>sample document</TITLE>\n    </HEAD>\n\n    <BODY BGCOLOR=\"FFFFFF\">\n    <HR>\n    <a href=\"http://google.com\">Goog</a>\n    <H1>This is one header</H1>\n    <H2>This is a another Header</H2>\n    <P>Travel from\n        <P>\n        <B>SFO to JFK</B>\n        <BR>\n        <B><I>on May 2, 2015 at 2:00 pm. For details go to confirm.com </I></B>\n        <HR>\n        <div style=\"color:#0000FF\">\n            <h3>Traveler <b> name </b> is\n            <p> John Doe </p>\n        </div>"

							__UpperCamelCase      :  Optional[int]       =   "\n    <!DOCTYPE html>\n    <html>\n    <body>\n\n    <h1>My First Heading</h1>\n    <p>My first paragraph.</p>\n\n    </body>\n    </html>\n    "

							return [html_string_a, html_string_a]




@require_bsa
class 					lowerCamelCase__				(							__lowercase	,		unittest.TestCase):




	'''simple docstring'''




	_A								=	MarkupLMFeatureExtractor if is_bsa_available() else None







	def   _lowerCamelCase							(   self						:List[str]		)						->					str:
								__UpperCamelCase      :  List[str]       =   MarkupLMFeatureExtractionTester(self		)







	@property
	def   _lowerCamelCase							(   self						:List[Any]		)						->					List[str]:
								return self.feature_extract_tester.prepare_feat_extract_dict()







	def   _lowerCamelCase							(   self						:Dict		)						->					Dict:
								# Initialize feature_extractor
								__UpperCamelCase      :  Tuple       =   self.feature_extraction_class()

								# Test not batched input
								__UpperCamelCase      :  Union[str, Any]       =   get_html_strings()[0]
								__UpperCamelCase      :  Any       =   feature_extractor(a		)

								# fmt: off
								__UpperCamelCase      :  Union[str, Any]       =   [["sample document", "Goog", "This is one header", "This is a another Header", "Travel from", "SFO to JFK", "on May 2, 2015 at 2:00 pm. For details go to confirm.com", "Traveler", "name", "is", "John Doe"]]
								__UpperCamelCase      :  Tuple       =   [["/html/head/title", "/html/body/a", "/html/body/h1", "/html/body/h2", "/html/body/p", "/html/body/p/p/b[1]", "/html/body/p/p/b[2]/i", "/html/body/p/p/div/h3", "/html/body/p/p/div/h3/b", "/html/body/p/p/div/h3", "/html/body/p/p/div/h3/p"]]
								# fmt: on

								self.assertEqual(encoding.nodes	,  a		)
								self.assertEqual(encoding.xpaths	,  a		)

								# Test batched
								__UpperCamelCase      :  str       =   get_html_strings()
								__UpperCamelCase      :  List[Any]       =   feature_extractor(a		)

								# fmt: off
								__UpperCamelCase      :  Optional[int]       =   expected_nodes + [["My First Heading", "My first paragraph."]]
								__UpperCamelCase      :  Tuple       =   expected_xpaths + [["/html/body/h1", "/html/body/p"]]

								self.assertEqual(len(encoding.nodes		)	,  2		)
								self.assertEqual(len(encoding.xpaths		)	,  2		)

								self.assertEqual(encoding.nodes	,  a		)
								self.assertEqual(encoding.xpaths	,  a		)